Simple reproduction of `aggr` examples using Pandas

pandas_groupby.ipynb

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   "source": [
    "Quick reproduction of examples from [`aggr` GitHub docs](https://github.com/Horb/aggr). "
   ]
  },
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   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before anything, save copies of the two example datasets -"
   ]
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     "text": [
      "Writing data.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile data.txt\n",
    "Eggs,12\n",
    "Chips,13\n",
    "Beans,14\n",
    "Eggs,21\n",
    "Chips,32\n",
    "Beans,43"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing data2.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile data2.txt\n",
    "2015-05-13,Eggs,1200\n",
    "2015-05-13,Chips,1300\n",
    "2015-05-13,Chips,1300\n",
    "2015-05-13,Bean,1300\n",
    "2015-05-15,Eggs,1300\n",
    "2015-05-15,Eggs,1300\n",
    "2015-05-15,Eggs,1300\n",
    "2015-05-15,Chips,1300\n",
    "2015-05-15,Beans,1300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Example 1) \n",
    "\n",
    "Aggregate sum over common key in `data.txt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        1\n",
      "0        \n",
      "Beans  57\n",
      "Chips  45\n",
      "Eggs   33\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data.txt', header=None)\n",
    "print df.groupby([0, ]).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2/3)\n",
    "\n",
    "Aggregate sum over first column, then by length of entry in second. It looks like there's an error in this example on the `aggr` github page; the output suggests there are no 5 character strings on 2015-05-13 and no 4 character strings on 2015-05-15.\n",
    "\n",
    "I bet the 2nd line below could be rolled into the groupby function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     count\n",
      "date       item_len       \n",
      "2015-05-13 4          2500\n",
      "           5          2600\n",
      "2015-05-15 4          3900\n",
      "           5          2600\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data2.txt', header=None, names=['date', 'item', 'count'])\n",
    "df['item_len'] = df['item'].apply(lambda x: len(x))\n",
    "\n",
    "print df.groupby(['date', 'item_len']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Composite keys..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     2\n",
      "0          1          \n",
      "2015-05-13 Bean   1300\n",
      "           Chips  2600\n",
      "           Eggs   1200\n",
      "2015-05-15 Beans  1300\n",
      "           Chips  1300\n",
      "           Eggs   3900\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data2.txt', header=None)\n",
    "print df.groupby([0, 1]).sum()"
   ]
  }
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